IG Established

Lack of interoperability between tools/e-infrastructures presents a significant barrier to streamlining processes throughout the research lifecycle. These gaps prevent the comprehensive collection and incorporation of research data and metadata into the research record captured during the active research phase. Furthermore, it limits the scope for passing this data and metadata on to data repositories, thus undermining FAIR data principles and reproducibility.

The FAIR for Machine Learning (FAIR4ML) Interest Group will enable community members to discuss the various aspects of FAIR as applied to Machine Learning, looking both at domain specific and domain-agnostic use cases, and creating task forces and working groups as needed for specific guidance documents, recommendations, definitions and technical specification to that effect. The overall aim is to foster collaborations among researchers and developers who are interested in making machine learning (data, models, workflows, etc.) FAIR, along with those who contribute to the infrastructure and policies that support this.

Aligning with RDA’s mission, the purpose of the Psychology RDA Interest Group (IG) is to raise awareness of the importance of data sharing, and to provide support with respect to the technical skills needed for FAIR data sharing within the Psychology community. This IG brings together Psychology researchers with an interest in open, transparent, and reproducible research methods and practices.

How to get involved with this interest Group?

• Join the RDA group and be part of the mailing list to get updates

Sensitive data is increasingly used for research purposes. However, there are a number of barriers which need to be overcome before sensitive data can be fully operationalized in the research context. Chief among these is that not all sensitive data is alike, with significant disciplinary variation in how sensitive data is defined, linked, managed, stored, and reused. Additionally, common approaches to data optimization are not always appropriate for sensitive data, such as the relationship between the FAIR and CARE principles for data management. Nonetheless, sensitive data has significant